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Article

Genetic Diversity of Fresh Maize Germplasm Revealed by Morphological Traits and SSR Markers

1
College of Agronomy, Qingdao Agricultural University, Qingdao 266109, China
2
College of Agronomy, Jilin Agricultural University, Changchun 130118, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Genes 2025, 16(10), 1138; https://doi.org/10.3390/genes16101138
Submission received: 1 September 2025 / Revised: 20 September 2025 / Accepted: 24 September 2025 / Published: 25 September 2025
(This article belongs to the Section Plant Genetics and Genomics)

Abstract

Background: This study aims to systematically evaluate the genetic divergence among 200 fresh maize inbred lines using both phenotypic and molecular markers, and to compare the efficacy of these two approaches for genetic classification. Methods: Phenotypic clustering analysis was conducted based on eight key agronomic traits, including plant height and ear length. Additionally, molecular analysis was performed using 40 Simple Sequence Repeat (SSR) primer pairs, resulting in the generation of 230 polymorphic alleles. The polymorphism information content (PIC) was calculated to evaluate the discriminatory power of the markers. Results: Phenotypic analysis categorized the inbred lines into four groups, comprising 25, 38, 97, and 40 lines, respectively, with benchmark lines distributed across Groups I and III. SSR analysis revealed a high level of genetic diversity, with an average of 5.75 alleles per locus and a mean polymorphic information content (PIC) of 0.70. Molecular grouping further divided the population into four distinct clusters, representing 26.5%, 51.0%, 14.0%, and 8.5% of the total, which exhibited different distribution patterns compared to the phenotypic grouping. The distribution of benchmark lines across various molecular groups confirmed their genetic divergence. Conclusions: SSR-based clustering demonstrated superior robustness and reliability compared to phenotypic grouping for genetic discrimination. These findings confirm the substantial genetic diversity present in fresh maize inbred lines and support the preferential use of SSR markers in maize breeding programs for precise genetic characterization.

1. Introduction

Fresh maize is a specialty type that is highly valued for its unique flavor and nutritional richness. Both its global cultivation area and market demand have experienced remarkable growth. Agricultural restructuring plays a significant role in increasing farmers’ income and meeting consumers’ demands for diverse agricultural products. Fresh maize is primarily categorized into three types: sweet corn, waxy corn, and sweet-waxy corn. From a genetic classification perspective, sweet corn can be further divided into normal sweet maize, super sweet maize (which includes sh2, bt1, and bt2 genotypes), and enhanced sweet maize (se) [1]. Waxy corn, also known as glutinous corn or waxy maize, is characterized by an endosperm starch composed almost entirely of amylopectin. This unique composition imparts a distinctive sticky texture and a soft, chewy consistency when cooked. In comparison to sweet corn, waxy corn exhibits a lower sugar content, typically ranging from 5% to 10%. This type originated from a recessive mutation in the Wx gene located on the short arm of chromosome 9 in common maize (Zea mays L.), which results in its distinct glutinous properties upon cooking. Based on kernel coloration, waxy corn can be classified into several types: white waxy corn, yellow waxy corn, purple waxy corn, black waxy corn, and multicolored waxy corn [2]. Among these, white and yellow waxy corn types are the most commonly consumed in daily life. Sweet-waxy corn is a hybrid type that combines the desirable traits of both sweet corn and waxy corn. This innovative type produces ears that contain both sweet and glutinous kernels on the same cob, thereby satisfying consumer demand for a dual-texture experience that offers both sweetness and chewiness [3,4].
In 2022, the cultivation area of fresh maize in China expanded significantly, reaching over 1.666 million hectares. This growth trajectory has persisted, further solidifying China’s position as the global leader in maize production. This consistent expansion is driven by steadily increasing yields and the maturation of cultivation technologies. Consequently, China has successfully established itself as the world’s largest producer of fresh maize, thereby securing a prominent position in the global fresh maize market [5].
The development of fresh maize types is essential for agricultural advancement. In hybrid breeding programs, the selection of appropriate parental lines is critical. Breeders generally cross elite parental lines that exhibit desirable traits, including early maturity, high yield, and superior quality, to create high-performance hybrid types.
Phenotypic trait evaluation, commonly referred to as morphological markers, serves as a method for assessing plant genetic diversity through the observation and statistical analysis of external morphological characteristics [6]. The investigation and classification of phenotypic traits represent the most intuitive approach. This method has been extensively utilized in preliminary species assessments [7,8,9]. To date, phenotypic studies have been conducted on various crops, including rice [10], wheat [11], maize [12], and mung bean [13,14]. However, phenotypic markers demonstrate significant limitations: the number of measurable traits is restricted, their expression is influenced by environmental factors, leading to genetically unstable manifestations, and they may obscure inherent genetic variation among alleles, as similar phenotypic expressions can arise from different alleles. With the advancements in molecular marker technologies, DNA-based detection methods have increasingly been adopted as standard practices by official institutions, such as the International Union for the Protection of New Varieties of Plants (UPOV). This trend is attributed to their higher detection efficiency, greater polymorphism, and enhanced genetic stability [15].
The relatively limited genetic diversity in Chinese maize germplasm primarily results from a long-term overreliance on four key inbred lines: The repeated utilization of Reid, Lancaster, Tangsipingtou, and Lüda Red Cob in breeding programs has resulted in a narrowed genetic base [16]. This genetic bottleneck not only constrains the discovery and exploitation of favorable alleles, thereby impeding the advancement of maize breeding in China, but it also contributes to cultivar degeneration and reduced stress tolerance. Ultimately, this undermines the resilience of the agricultural system to natural disasters [17]. Consequently, the enrichment and innovation of germplasm through the accumulation of novel genetic resources has emerged as a critical objective for maize breeding research and development in China.
This study systematically evaluates the genetic diversity of fresh maize germplasm resources in China and investigates the difference between morphological markers and SSR molecular marker analyses. This research hypothesizes that, despite a potentially narrow overall genetic base in existing germplasm, strong selection for specific consumer-oriented traits, such as sweetness and waxiness, has driven divergence at key genomic loci. Consequently, we propose that the three flavor types-sweet corn, waxy corn, and sweet-waxy corn-may represent distinct genetic groups, defined more by these selected regions than by overall genetic variability. By integrating both phenotypic and molecular-level data analyses, we aim to establish a comprehensive phenotype-genotype database comprising 200 inbred lines. This initiative will elucidate the characteristics of population structure and genetic relationships, ultimately facilitating the identification of parental materials with breeding potential. Furthermore, it will provide a theoretical foundation for the selection of hybrid combinations in fresh maize.

2. Materials and Methods

2.1. Materials

A total of 200 fresh maize inbred lines, representing a diverse range of genetic backgrounds, were collected from various geographical regions and uniformly designated as GM001 to GM200. This collection includes four benchmark inbred lines: GM002 (Ye478), GM003 (Dan340), GM007 (Mo17), and GM171 (Qi319), which serve as standard testers. The remaining 196 accessions represent newly acquired germplasm materials sourced from various production areas. The experimental materials utilized in this study were procured from the following suppliers: primers from Sangon Biotech (Shanghai) Co., Ltd., Shanghai, China, and other reagents from Yicheng Chemical Reagent Business Department, Tai’an, China.

2.2. Methods

2.2.1. Field Experiment Design, Phenotypic Trait Investigation, and Seed Character Evaluation Methods

In mid-June 2022, all 200 experimental materials were planted at two locations: the Mizhou Seed Industry Experimental Base in Zhucheng, Weifang, Shandong Province (36°23′ N, 119°24′ E) and the experimental field in Pingdu, Qingdao, Shandong Province (36°47′ N, 119°58′ E). The trial employed a non-replicated design, with each plot consisting of two rows containing twenty plants per row. The planting configuration maintained a spacing of 60 cm between rows and 30 cm between plants within each row. Throughout the entire growth cycle of the fresh maize inbred lines, field management practices—including intertillage, irrigation and fertilization regulation, as well as pest and disease control—were strictly implemented in accordance with local commercial production standards. The total experimental area at each site was approximately 800 square meters. From the central area of each accession’s plot, five representative plants exhibiting uniform growth and free from pests or diseases were systematically selected.
During the experimental period, three categories of phenotypic traits were systematically investigated: field performance traits, agronomic traits, and yield component traits. Field performance traits encompass the observable and measurable characteristics of crops during growth in field conditions. These traits are crucial for evaluating plant growth status, yield potential, stress resistance, and quality parameters. These traits serve as fundamental indicators for monitoring crop development and adaptation. Agronomic traits represent measurable characteristics associated with crop growth dynamics, yield formation processes, and quality attributes throughout the production cycle. As critical benchmarks for assessing cultivar performance and the effectiveness of cultivation management, these traits provide essential guidance for agricultural production practices aimed at enhancing both the quantity and quality of yield. Yield-component traits encompass all characteristics that directly or indirectly influence crop productivity. These critical traits predominantly determine the final yield output and thus serve as primary objectives in crop genetic improvement and varietal development programs. The measurement and optimization of these traits are essential for achieving breeding targets and enhancing agricultural productivity. In this study, plant height, ear height, tassel length, and the number of tassel branches are classified as field performance traits. Ear length and ear diameter are categorized as agronomic traits. The number of kernel rows and the number of kernels per row are identified as yield-component traits.

2.2.2. Statistical Methods for Phenotypic Traits

Two hundred fresh maize inbred lines were classified into distinct groups through cluster analysis based on eight key phenotypic traits: plant height, ear height, tassel length, tassel branch number, ear length, ear diameter, kernel row number, and kernel number per row. The analysis was conducted using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm, implemented in R software (version 4.2.1). This hierarchical clustering approach facilitated the systematic grouping of inbred lines based on their phenotypic similarities. The resulting dendrogram illustrates the genetic relationships among various accessions.

2.2.3. SSR Analysis and Data Processing Methods

(1)
In the analysis of genetic diversity, bands obtained through SSR silver staining were scored using a co-dominant scoring system. Homozygous loci were recorded with their corresponding allele fragment sizes (e.g., “215/215”), while heterozygous loci were recorded with both allele fragment sizes (e.g., “215/221”). Missing data were denoted as “9”. This scoring facilitated the construction of a comprehensive database. The genetic similarity coefficient (GS) and genetic distance (GD) between fresh maize inbred lines were calculated using Nei’s formula [18]. The specific formulas are expressed as follows: GSij = 2Nij/(Ni + Nj) and GDij = 1 − GSij. Here, Nij represents the number of bands shared between inbred lines *i* and *j*, while Ni and Nj denote the total number of bands present in inbred lines *i* and *j*, respectively. These equations are critical for understanding the genetic similarities and differences among various inbred lines. After completing the calculations, a cluster analysis was performed using the UPGMA method to construct a dendrogram. The polymorphism information content (PIC) of each polymorphic locus was calculated according to the formula proposed by Smith et al. [19]: PIC = 1 − ∑fi2, where fi represents the allele frequency at locus i.
(2)
The IBM SPSS Statistics software (version 27.0) was employed to compare the phenotypic traits of fresh maize inbred lines for significant differences.

3. Results

3.1. Phenotypic Cluster Analysis

From June to September 2022, eight phenotypic traits were examined in 200 fresh maize inbred lines: plant height, ear height, tassel length, tassel branch number, ear length, ear diameter, kernel row number, and kernel number per row. Statistical analyses of the agronomic traits presented in Table 1 revealed significant genetic diversity across all measured characteristics. For the phenotypic cluster analysis, the average values of the eight traits from both locations were utilized (refer to Appendix A). The results demonstrated substantial variation, thereby providing a robust foundation for further assessments of genetic diversity and breeding applications.
The UPGMA (Unweighted Pair Group Method with Arithmetic Mean) cluster analysis was conducted using the comprehensive data presented in Table A1, with the results illustrated in Figure 1. This analysis demonstrated that the 200 fresh maize inbred lines were categorized into four primary clusters: Cluster I comprised 25 inbred lines, accounting for 12.50% of the total sample; Cluster II included 38 inbred lines, representing 19.00% of the total sample; Cluster III contained 97 inbred lines, constituting the largest proportion at 48.50%; and Cluster IV consisted of 40 inbred lines, making up 20.00% of the total sample. The detailed results of the phenotypic cluster analysis are presented in Table 2. This classification highlights significant phenotypic variation among the fresh maize inbred lines, with Cluster III identified as the most predominant group. The observed distribution patterns indicate potential genetic relationships and phenotypic diversity within the germplasm collection.
The results of the phenotypic cluster analysis indicated that 48.50% of the fresh maize inbred lines were categorized into Cluster III. Notably, the benchmark inbred lines GM002 and GM003 were classified within Cluster I, whereas GM007 and GM171 were assigned to Cluster III. These findings indicate that classification based solely on phenotypic traits has inherent limitations in accuracy, rendering it insufficient for the precise categorization of maize inbred lines. For comprehensive data, please refer to Figure 1.

3.2. Genetic Distance Analysis

Genetic distance analysis of 200 fresh maize inbred lines revealed a range of genetic distances from 0.308 to 0.902, with an average genetic distance of 0.657. This average value suggests that there are relatively distant genetic relationships among these inbred lines overall. Through further analysis of the data, the genetic differences among the research materials are clearly delineated in Table 3 and Table 4. As shown in Table 3, the genetic distance between the inbred lines GM013 and GM021 was only 0.308, the smallest among all pairs of inbred lines, indicating that they share closely related genetic backgrounds. Conversely, Table 4 indicates that the inbred lines GM176 and GM184 exhibited the highest genetic distance of 0.902 among all pairs, thereby demonstrating a significant genetic divergence between these two lines.

3.3. SSR Genetic Diversity and Cluster Analysis

3.3.1. Polymorphism Analysis of SSR Markers

PCR amplification was conducted using 40 SSR primer pairs on 200 fresh maize inbred lines, resulting in the detection of a total of 230 polymorphic alleles. The number of alleles identified per primer pair varied from 2 to 9, with an average of 5.75 alleles per locus. The polymorphism information content (PIC) of the primers ranged from 0.30 to 0.83, yielding an average value of 0.70, which indicates a high level of genetic diversity within the population. Notably, the primers umc2105g1 (PIC = 0.82) and bnlg2305g1 (PIC = 0.83) exhibited the highest levels of polymorphism, each identifying nine distinct alleles. The average heterozygosity rate among the inbred lines was 2.1%, indicating a high degree of genetic homozygosity in the materials. Figure 2 presents the electrophoretic separation patterns of PCR products amplified by the primer umc2105g1 across various inbred lines, whereas Figure 3 illustrates those amplified by the primer bnlg2305g1. The observed clear banding polymorphisms and significant differences in fragment sizes reflect the genetic variation among the materials. These results confirm that SSR markers are effective in revealing allelic diversity within the fresh maize population.
The analysis of the SSR marker polymorphism results indicated that the effective number of alleles per locus ranged from 1.71 to 7.92, with an average value of 4.93. This indicates both an uneven distribution of alleles within the population and substantial genetic diversity among the 200 inbred lines. Notably, primer bnlg2305g1 exhibited the highest level of polymorphism (PIC = 0.83), underscoring its remarkable ability to reveal genetic variations among maize materials. Comprehensive data are presented in Table 5.

3.3.2. SSR Marker-Based Cluster Analysis

The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) was utilized to analyze 200 fresh maize inbred lines, resulting in their classification into four distinct clusters. Cluster I consisted of 53 inbred lines, representing 26.50% of the total samples. Cluster II, the largest group, comprised 102 inbred lines, accounting for 51.00% of the total. Cluster III consisted of 28 inbred lines, representing 14.00%, while Cluster IV included 17 inbred lines, making up 8.50% of the total. The detailed clustering results are illustrated in Figure 4, which demonstrates the genetic relationships and population structure among the fresh maize inbred lines.
As illustrated in Table 6, the benchmark selfing lines GM002, GM003, GM007, and GM171 were categorized into distinct taxa. In the study, GM003 (Dan 340) was classified as the first taxon, GM002 (Ye 478) as the second taxon, GM007 (M017) as the third taxon, and GM171 (Qi 319) as the fourth taxon. The four benchmark inbred lines can be accurately classified into their respective lineage taxa. Among all the fresh maize germplasm, GM013 and GM021 exhibited the shortest genetic distance, resulting in their clustering within the second taxon. In contrast, GM176 and GM184 displayed the greatest genetic distance, leading to their categorization into the second and fourth taxa, respectively. Based on the clustering results, it is evident that the clustering analysis utilizing SSR molecular markers can more accurately reflect the genetic associations among various fresh maize germplasm and elucidate their affinities.

4. Discussion

The SSR molecular marker technique has been extensively utilized in genetic diversity studies of maize germplasm owing to its high polymorphism, co-dominant inheritance, operational simplicity, and relatively low cost [20,21]. Wang et al. [22] analyzed 35 waxy maize inbred lines and 5 common maize types using SSR markers, successfully classifying them into 4 distinct groups consistent with their pedigree origins. Li et al. [23] characterized 144 sweet maize accessions, identifying seven major genetic clusters and 343 allelic variants, with a range of 4 to 17 alleles per locus and a mean of 8.58 alleles. Huang et al. [24] evaluated 54 sweet maize inbred lines using 56 SSR primers, revealing three primary genetic groups and 155 polymorphic loci. Lu et al. [25] assessed 87 fresh-eating maize inbred lines using 29 SSR markers. Their analysis revealed four genetic clusters and identified a total of 180 allelic variants, with a mean of 6 alleles per locus. The PIC ranged from 0.308 to 0.915. Zhao [26] conducted SSR analysis on 100 fresh maize germplasms, classifying them into six major genetic groups.
This study conducted a cluster analysis on eight major agronomic traits across 200 fresh maize inbred lines. The selected traits, consistent with the research of Tan et al. [27], exhibited highly significant variation. Following the methodology established by Marwa et al. [28], phenotypic traits served as critical criteria for germplasm evaluation. The test materials exhibited substantial genetic diversity, with clustering results categorizing them into four major groups. Notably, four benchmark inbred lines were distributed between Cluster I and Cluster III, indicating the limitations of relying solely on phenotypic clustering.
The application of SSR molecular marker technology has become increasingly prevalent in the breeding of fresh maize. SSR molecular markers are characterized by their high accuracy, enabling precise delineation of inbred lines of fresh maize. Qiu et al. [29] utilized molecular and morphological markers to examine the differences in quality and agronomic traits of fresh maize across various genetic backgrounds. Their study analyzed 41 test materials, revealing significant variation in both agronomic and quality traits, along with a high level of genetic diversity in SSR markers. The coefficients of variation for 12 agronomic and quality traits ranged from 1.72% to 36.10%, with a mean value of 14.06%. A total of 321 alleles were detected using 40 SSR markers, exhibiting polymorphic information content that varied from 0.179 to 0.866, with a mean value of 0.658. Additionally, gene diversity ranged from 0.186 to 0.877, with a mean value of 0.687. In this study, we utilized 40 pairs of SSR primers exhibiting high polymorphism and stable banding patterns to analyze the genetic diversity of 200 fresh maize inbred lines. A total of 230 allelic loci were detected, with the number of alleles per primer pair ranging from 2 to 9, averaging 5.75. The mean value of the polymorphism information content (PIC) was found to be 0.70. Among the primers, umc2105g1 and bnlg2305g1 exhibited the highest number of allelic loci. Notably, bnlg2305g1 not only had the highest number of allelic sites but also recorded the highest Polymorphic Information Content (PIC) value. This suggests that SSR molecular marker technology is particularly effective in detecting genetic polymorphisms in maize [30]. In this study, 200 fresh maize inbred lines with diverse genetic backgrounds were classified into four primary clusters through UPGMA cluster analysis. Comparison with phenotypic clustering revealed that SSR molecular marker technology is more accurate in classifying maize inbred line taxa. Additionally, the classification of self-inherited line germplasm using this marker can categorize maize germplasm resources from various sources into distinct lineages, thereby providing a foundation for future hybrid breeding. Numerous studies have demonstrated that SSR marker technology is extensively utilized in the fields of plant genetics and germplasm resource research, providing a valuable reference for subsequent related investigations [31,32,33].
The estimation of genetic distance is a crucial aspect of the comprehensive study of crop type origins and the elucidation of kinship relationships among populations [34,35]. The genetic distance analysis of 200 fresh maize inbred lines revealed that the inbred lines GM013 and GM021 share a closer kinship relationship and genetic background. In contrast, the inbred lines GM176 and GM184 exhibited significant genetic divergence, indicating that they are more distantly related and may serve as effective parental materials for the breeding of new maize types. There are significant genetic differences between the autografts GM176 and GM184, indicating their more distant relationship, enhanced hybrid vigor, and greater genetic diversity. These characteristics suggest that they can serve as parental lines for the cultivation of new maize autograft types [36,37].

5. Conclusions

A clustering analysis was conducted on eight phenotypic traits of 200 fresh maize inbred lines, resulting in the classification of the materials into four major clusters. The first cluster comprised 25 inbred lines, including the benchmark lines GM002 (Ye 478) and GM003 (Dan 340). The second cluster contained 38 inbred lines, while the third cluster encompassed 97 inbred lines, including the benchmark lines GM007 M017 and GM171 Qi 319. The fourth cluster included 40 inbred lines. Notably, the third cluster represented 48.5% of the total inbred lines analyzed.
A total of 230 allelic loci were detected using SSR molecular markers, with an average of 5.75 alleles identified per primer pair. The mean polymorphism information content (PIC) value was found to be 0.70. Genetic distance analysis revealed significant differences among the self-inbred lines, with GM013 and GM021 exhibiting the smallest genetic distance, while GM176 displayed the largest genetic distance from GM184.
SSR clustering divided the materials into four major clusters, with the benchmark inbred lines GM002, GM003, GM007, and GM171 distributed across different clusters. Compared to phenotypic clustering, the SSR clustering results demonstrated a higher reliability in effectively differentiating distinct clusters. Additionally, 200 fresh maize inbred lines exhibited significant genetic diversity.

Author Contributions

Conceptualization, S.G. and X.Z.; methodology, S.W. and Y.A.; software, S.G. and X.Z.; validation, S.G. and X.Z.; formal analysis, S.W. and Y.A.; investigation, S.G. and X.Z.; writing—original draft preparation, S.G. and X.Z.; writing—review and editing, S.G. and X.Z.; visualization, S.G. and X.Z.; supervision, R.Z.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

Shandong Provincial Key Research and Development Program (Competitive Innovation Platform) Project (Project No.: 2024CXPT020).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

We extend our sincere gratitude to all members of our laboratory for their collective efforts.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SSRSimple Sequence Repeat
PICPolymorphism Information Content
GSGenetic Similarity Coefficient
GDGenetic Distance

Appendix A

Table A1. Mean traits of 200 fresh maize inbred lines.
Table A1. Mean traits of 200 fresh maize inbred lines.
NumberPlant Height (cm)Ear Height (cm)Tassel Length
(cm)
Tassel Branch Number
(Number)
Ear Length
(cm)
Ear Diameter (cm)Kernel Row Number
(Rows)
Kernel Number Per Row (Kernels)
GM001184692868.23.91621
GM002176612171441420
GM0031655426565.31818
GM00419572311215.34.91218
GM00515552191112.23.91416
GM0061113511412.93.31214
GM00719058279164.81623
GM0081455535129.64.31216
GM00916863211815.23.11415
GM01017568311716.14.21822
GM01118071311618.13.61828
GM0121465828913.24.11418
GM0131344024411.63.71214
GM01416857261011.14.31420
GM01519765311214.14.21418
GM0162108529411.94.61820
GM01722195301716.84.81625
GM018187653015174.61425
GM01918689291618.23.11422
GM02014550271213.44.21224
GM021123521757.83.21215
GM0221565428812.541416
GM023176773016203.51632
GM0241264025612.82.61418
GM02519083351217.53.51228
GM02619570321417.14.31426
GM0271676131812.53.11219
GM02818470251418.24.51628
GM02918291321418.73.11627
GM03019679331016.23.81424
GM03122188312016.54.61826
GM03219081321316.43.51226
GM03318570351517.73.81623
GM0341757533913.33.91622
GM0351646030710.34.21219
GM0361555133917.23.91624
GM03714347301115.73.71822
GM038134551811143.21420
GM039170561191031216
GM040172602013103.41418
GM0411696635912.23.61625
GM04221065271514.54.61216
GM04320572321513.541415
GM0441183521711.53.91416
GM04521585321617.34.81625
GM0461646531610.63.41218
GM0471747529912.24.11426
GM048176712110134.21420
GM04919885351315.54.11424
GM050183101281113.441623
GM05122486371318.14.41628
GM0521244625811.83.71220
GM0531686432711.53.21419
GM0541787531912.141422
GM05519894372118.54.11632
GM05620171281014.53.61416
GM05721087291615.15.11825
GM0581856724912.94.91628
GM05921689251413.94.21230
GM06022683378164.41423
GM0611383621916.24.11424
GM062158753169.84.61218
GM06319595368113.81416
GM064210109402316.34.51835
GM06517280291011.84.41622
GM0661435226911.33.11028
GM0671616329812.24.11424
GM06817169339113.91624
GM069186102261012.84.11427
GM07015656351213.53.61416
GM07119466341415.64.31420
GM07215354331512.73.11216
GM073173813291341622
GM07418972311416.83.91627
GM0751706522714.24.71426
GM07619167331213.54.21218
GM07719595351318.94.11224
GM078187102311312.43.91423
GM07914451291114.13.51226
GM08019487331715.33.51428
GM0812041003319134.11833
GM08220685371717.83.81224
GM08318762261117.35.41630
GM08419398347133.51218
GM08518573381618.23.91428
GM08618198311113.24.31625
GM08715473308104.11619
GM08819770301314.54.71418
GM08921483351313.63.91218
GM090228103352015.34.11630
GM09118996291412.34.71827
GM09221199372316.741633
GM09311942271113.43.61418
GM09417676311213.73.61424
GM0951284025710.43.81216
GM09611442231112.13.51216
GM0971195826913.74.91620
GM09822369401315.53.91414
GM09920081351616.23.31624
GM1001495622713.63.91422
GM10118981302319.24.51628
GM10217861371211.33.71214
GM10317063259114.71420
GM1041326522119.53.61418
GM105155792669.54.71415
GM10614756251114.23.61224
GM107229992919174.51832
GM1081656331912.63.61417
GM1091686134711.13.31218
GM1101576020310.94.41615
GM1111606033612.33.21417
GM1121184124910.13.31014
GM1131817327612.93.21222
GM11419083251716.93.91424
GM1151354025511.33.61216
GM11614650241011.23.71226
GM11711839241013.43.91216
GM11816654269104.71421
GM1191526126912.34.31419
GM12018060381314.64.41417
GM1211596425913.34.31620
GM12220663231514.33.51414
GM123124561559.13.81417
GM1241274426712.13.91215
GM12519671291513.54.21418
GM1261494327610.23.81015
GM1271566026913.54.51620
GM12817978329124.11424
GM12918265361215.34.21218
GM1301454816711.33.51422
GM13116253279125.2168
GM132138602013113.51216
GM1331434427710.63.21214
GM1341787331913.34.21226
GM1351384522913.43.31422
GM1361996729121441416
GM13714355199113.21420
GM1381666529512.63.41219
GM13921190312416.54.21230
GM140188100271012.74.51823
GM14115569221112.63.81226
GM142177702410114.11231
GM1431485526913.63.41027
GM14419579361318.53.91431
GM14517569291012.24.41619
GM1461736218911.54.71424
GM1471454522511.23.41015
GM14819079311415.73.91426
GM14916763349123.71219
GM1501264616912.23.51416
GM1511695928711.84.41617
GM15217168221214.64.61426
GM15322385391116.64.11628
GM1541214026811.33.91415
GM155211100301917.13.91630
GM1561605829613.54.21619
GM15713550251013.23.31027
GM1582139532812.94.91420
GM15917060221011.94.31222
GM160198833011164.31828
GM16117564201112.44.11222
GM16219392391717.65.21432
GM163125511777.53.11215
GM16420579251212.94.71820
GM16518867401113.63.91416
GM16617770261113.84.31622
GM16711637221213.33.71216
GM1681665624511.34.91418
GM16915556261012.53.31225
GM1701485923815.13.61224
GM17115854291110.53.41426
GM17217469221314.34.21626
GM17314657281313.94.11424
GM17422087361516.94.71234
GM17511339231311.23.41416
GM17623490391517.24.61430
GM17714256311211.43.91222
GM1781336120109.63.81416
GM1791886923912.94.91824
GM18011733261213.53.71415
GM18116559271115.24.21219
GM18220084341317.14.21626
GM1831294322713.241416
GM1842069231512.24.71620
GM185145522911143.51229
GM18622288391518.24.51432
GM1872059335512.34.31620
GM1881686535911.13.11619
GM18920379381211.74.61628
GM1901616032512.43.41419
GM1911376523139.43.51416
GM19217667231214.34.31224
GM1931989135715.33.91622
GM19415160239113.81416
GM195213104373913.64.71828
GM1961696728610.23.11218
GM197125391459.63.51216
GM1981525830912.94.11222
GM19914762219114.31416
GM2001938531814.54.21826

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Figure 1. Phenotypic clustering diagram of 200 fresh maize inbred lines. The four major clusters are labeled with Roman numerals (I–IV). The scale bar represents branch lengths in units of genetic distance. Negative values on the scale are mathematical artifacts resulting from the tree-rooting process and have no biological significance. The genetic distance between any two inbred lines is proportional to the sum of the horizontal branch lengths along the path connecting them. C1: Plant height, C2: Ear height, C3: Tassel length, C4: Tassel branch number, C5: Ear length, C6: Ear diameter, C7: Kernel row number, C8: Kernel number per row.
Figure 1. Phenotypic clustering diagram of 200 fresh maize inbred lines. The four major clusters are labeled with Roman numerals (I–IV). The scale bar represents branch lengths in units of genetic distance. Negative values on the scale are mathematical artifacts resulting from the tree-rooting process and have no biological significance. The genetic distance between any two inbred lines is proportional to the sum of the horizontal branch lengths along the path connecting them. C1: Plant height, C2: Ear height, C3: Tassel length, C4: Tassel branch number, C5: Ear length, C6: Ear diameter, C7: Kernel row number, C8: Kernel number per row.
Genes 16 01138 g001
Figure 2. Electrophoregram of PCR amplification products of primer umc2105g1 in 60 fresh maize inbred materials (M: Marker, GM002 to GM061). The marker in Figure 2 is identical to that in Figure 3.
Figure 2. Electrophoregram of PCR amplification products of primer umc2105g1 in 60 fresh maize inbred materials (M: Marker, GM002 to GM061). The marker in Figure 2 is identical to that in Figure 3.
Genes 16 01138 g002
Figure 3. Electrophoregram of PCR amplification products of primer bnlg2305g1 in 60 fresh maize inbred materials (M: Marker, GM122 to GM181). The marker in Figure 3 is identical to that in Figure 2.
Figure 3. Electrophoregram of PCR amplification products of primer bnlg2305g1 in 60 fresh maize inbred materials (M: Marker, GM122 to GM181). The marker in Figure 3 is identical to that in Figure 2.
Genes 16 01138 g003
Figure 4. The dendrogram of 200 fresh maize inbred lines with SSR markers.
Figure 4. The dendrogram of 200 fresh maize inbred lines with SSR markers.
Genes 16 01138 g004
Table 1. Statistical parameters of different agronomic traits of 200 fresh maize inbred lines.
Table 1. Statistical parameters of different agronomic traits of 200 fresh maize inbred lines.
TraitsMinimum ValueMaximum ValueMean Value
Plant height (cm)104236172
Ear height (cm)2811467
Tassel length (cm)84628
Tassel branch number32211
Ear length (cm)8.122.413.3
Ear diameter (cm)2.66.44
Kernel row number82014
Kernel number per row63621
Table 2. Results of phenotypic clustering analysis.
Table 2. Results of phenotypic clustering analysis.
FormsQuantitiesSerial Number
Cluster I25GM003, GM127, GM121, GM166, GM145, GM065, GM087, GM156, GM151, GM119
GM012, GM022, GM048, GM002, GM100
GM062, GM035, GM105, GM118, GM103, GM014, GM168, GM110, GM097, GM131
Cluster II38GM005, GM191, GM104, GM178, GM132, GM199, GM194, GM040, GM137, GM130, GM150, GM135, GM038, GM039, GM163
GM021, GM197, GM123, GM006, GM124, GM095, GM115, GM013, GM052, GM183, GM044, GM154, GM180, GM093, GM175, GM167, GM117, GM096, GM147, GM126, GM133, GM112, GM024
Cluster III97GM004, GM187, GM184, GM016, GM158,
GM140, GM091, GM153, GM051, GM182
GM200, GM160, GM193, GM060, GM086
GM050, GM078, GM069, GM059, GM165,
GM098, GM071, GM129, GM076, GM089
GM125, GM043, GM136, GM056, GM088,
GM007, GM042, GM084, GM063, GM161,
GM192, GM141, GM142, GM128, GM054,
GM047, GM067, GM173, GM020, GM198
GM177, GM181, GM170, GM106, GM079,
GM169, GM116, GM157, GM143, GM066,
GM171, GM046, GM027, GM138, GM196
GM113, GM190, GM111, GM108, GM070
GM149, GM102, GM072, GM008, GM068
GM073, GM034, GM188, GM011, GM001,
GM122, GM009, GM036, GM061, GM152,
GM075, GM146, GM172, GM179, GM164
GM189, GM120, GM015, GM159, GM134,
GM185, GM109, GM053, GM041, GM037,
GM058, GM083
Cluster IV40GM025, GM186, GM176, GM174, GM162,
GM139, GM045, GM017, GM057, GM107,
GM031, GM101, GM155, GM090, GM092
GM055, GM064, GM195, GM081, GM029,
GM019, GM099, GM080, GM023, GM148,
GM094, GM030, GM049, GM032, GM082
GM077, GM144, GM085, GM026, GM018
GM028, GM114, GM074, GM033, GM010
Table 3. Pairwise genetic distance matrix among fresh maize inbred Lines (GM010–GM023).
Table 3. Pairwise genetic distance matrix among fresh maize inbred Lines (GM010–GM023).
NumberGM-019GM-020GM-021GM-022GM-023
GM-0100.454
GM-0110.5610.471
GM-0120.5220.5020.411
GM-0130.5090.5710.3080.702
GM-0140.4210.4050.4760.5590.629
Note: The smallest genetic distance was observed between GM013 and GM021.
Table 4. Pairwise genetic distance matrix among fresh maize inbred Lines (GM174–GM186).
Table 4. Pairwise genetic distance matrix among fresh maize inbred Lines (GM174–GM186).
NumberGM-182GM-183GM-184GM-185GM-186
GM-1740.4750.6610.7130.6220.578
GM-1750.5620.5720.4770.5320.607
GM-1760.5950.8290.9020.6580.798
GM-1770.6960.6110.4890.7970.574
GM-1780.4980.5970.6320.5120.482
Note: The largest genetic distance was also observed between GM176 and GM184.
Table 5. Allelic and polymorphic information content of fresh maize detected by SSR.
Table 5. Allelic and polymorphic information content of fresh maize detected by SSR.
NumberPrimer NameChromosomal
Location
Number of Polymorphic BandsEffective Number of Alleles Gene
Diversity
Polymorphism
Information
Content
1bnlg439g11.0386.250.840.73
2umc1335g21.0642.850.630.31
3umc1147g31.0775.010.820.68
4bnlg1671g41.163.990.740.61
5phi96100g32.0174.890.790.72
6umc2007g22.0454.030.620.58
7umc1536g42.0733.190.750.69
8bnlg1940g12.0886.990.880.79
9bnlg1520g42.0944.720.810.73
10umc2105g1397.020.860.82
11phi053g23.0575.180.770.7
12umc1489g33.0763.980.780.76
13phi072g1443.760.710.64
14bnlg490g34.0453.990.810.74
15bnlg2291g24.0743.040.670.56
16umc1999g34.0975.940.770.74
17umc2115g45.0255.010.750.71
18umc1705g25.0376.960.820.79
19umc1429g35.0387.920.840.81
20bnlg2305g15.0796.320.860.83
21bnlg161g2654.990.780.75
22bnlg249g46.0176.380.840.8
23bnlg1702g16.0565.630.780.73
24phi299852g36.0754.970.760.72
25umc1545g1765.730.750.71
26umc2160g47.0153.950.750.71
27umc1936g47.0365.280.760.74
28umc1125g27.0454.760.770.74
29bnlg2235g48.0276.460.810.79
30bnlg240g18.0687.510.830.81
31phi080g28.0865.390.790.76
32phi233376g38.0954.650.740.72
33umc2084g39.0165.510.780.75
34phi065g19.0343.740.730.7
35umc1492g29.0431.750.420.3
36umc1231g49.0564.860.780.73
37phi041g41021.710.470.42
38umc1432g110.0232.210.620.58
39umc2163g310.0454.680.760.71
40umc1506g210.0576.010.820.79
Table 6. Results of SSR molecular marker clustering.
Table 6. Results of SSR molecular marker clustering.
FormsQuantitiesSerial Number
Cluster I53GM001, GM003, GM005, GM017, GM023, GM024, GM030,
GM036, GM044, GM050, GM052, GM053, GM055, GM058,
GM066, GM072, GM075, GM080, GM085, GM086, GM087,
GM088, GM090, GM091, GM095, GM097, GM099, GM100,
GM106, GM109, GM111, GM112, GM115, GM122, GM125,
GM130, GM133, GM140, GM143, GM147, GM148, GM152,
GM153, GM161, GM165, GM168, GM175, GM177, GM178,
GM181, GM188, GM190, GM192
Cluster II102GM002, GM004, GM006, GM008, GM009, GM011, GM013,
GM014, GM015, GM016, GM018, GM019, GM020, GM021,
GM022, GM026, GM027, GM028, GM029, GM031, GM033,
GM034, GM035, GM038, GM039, GM041, GM042, GM046,
GM047, GM049, GM054, GM056, GM057, GM060, GM061,
GM062, GM063, GM065, GM067, GM069, GM071, GM074,
GM077, GM078, GM079, GM082, GM083, GM084, GM094,
GM096, GM098, GM102, GM103, GM104, GM110, GM113,
GM117, GM118, GM119, GM120, GM121, GM123, GM124,
GM126, GM127, GM128, GM131, GM134, GM135, GM136,
GM137, GM138, GM144, GM145, GM146, GM151, GM156,
GM159, GM162, GM163, GM164, GM166, GM167, GM169,
GM172, GM176, GM179, GM180, GM182, GM183, GM185,
GM187, GM189, GM191, GM193, GM194, GM195, GM196,
GM197, GM198, GM199, GM200
Cluster III28GM007, GM010, GM012, GM025, GM032, GM037, GM040,
GM045, GM048, GM051, GM059, GM068, GM070, GM073,
GM076, GM089, GM092, GM093, GM101, GM105, GM107,
GM108, GM114, GM116, GM132, GM149, GM150, GM160
Cluster IV17GM043, GM064, GM081, GM129, GM139, GM141, GM142,
GM154, GM155, GM157, GM158, GM170, GM171, GM173,
GM174, GM184, GM186
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Guo, S.; Zheng, X.; Wang, S.; Ai, Y.; Zhao, R.; Lan, J. Genetic Diversity of Fresh Maize Germplasm Revealed by Morphological Traits and SSR Markers. Genes 2025, 16, 1138. https://doi.org/10.3390/genes16101138

AMA Style

Guo S, Zheng X, Wang S, Ai Y, Zhao R, Lan J. Genetic Diversity of Fresh Maize Germplasm Revealed by Morphological Traits and SSR Markers. Genes. 2025; 16(10):1138. https://doi.org/10.3390/genes16101138

Chicago/Turabian Style

Guo, Suying, Xin Zheng, Shuaiyi Wang, Yuran Ai, Rengui Zhao, and Jinhao Lan. 2025. "Genetic Diversity of Fresh Maize Germplasm Revealed by Morphological Traits and SSR Markers" Genes 16, no. 10: 1138. https://doi.org/10.3390/genes16101138

APA Style

Guo, S., Zheng, X., Wang, S., Ai, Y., Zhao, R., & Lan, J. (2025). Genetic Diversity of Fresh Maize Germplasm Revealed by Morphological Traits and SSR Markers. Genes, 16(10), 1138. https://doi.org/10.3390/genes16101138

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